Point sets joint registration and co-segmentation

Abstract

We present a novel approach of joint registration and co-segmentation for point sets where objects move in different ways. We consider joint registration and co-segmentation as two problems that are heavily entangled with each other; thus, we represent the input point sets as samples from a generative model and bring up with a novel formulation based on Gaussian mixture model. By maximizing the posterior probability of the samples, we gradually recover the latent object models as well as an object-level segmentation and simultaneously align the segmented points to the latent object models. Along with the formulation, we design an interactive tool that helps users intuitively intervene the process to optimize the registration and segmentation results. The experiment results on a group of synthetic and scanned point clouds demonstrate that our method is powerful and effective for joint registration and co-segmentation on point sets of multiple objects.

Keywords

Notes

Acknowledgements

We would like to thank YantingLin and Jian Wu. They helped with data preparation for our experiments. We would also like to thank the National Natural Science Foundation for their funding. This study was funded by the National Natural Science Foundation of China under Nos. 61472377, 61632006, and 6133101.

Compliance with ethical standards

Conflict of interest

Siyu Hu declares that he has no conflict of interest. Xuejin Chen has received research Grants from Microsoft and Huawei Technology Co. Ltd. Xuejin Chen had visited Leonidas Guibass Group in Stanford University during February 21 to August 20, 2017. Xin Tong is researcher of Microsoft. He is associate editor of ACM TOG and IEEE TVCG. He is also guest professor of University of Science and Technology of China and Tianjin University.